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Natural Language Elevates Coding, Planning, Robotics Performance

Natural language boosts LLM performance in coding, planning, and robotics

MIT CSAIL researchers create three neurosymbolic methods to help language models build libraries of better abstractions within natural language: LILO assists with code synthesis, Ada helps with AI planning, and LGA guides robotic navigation and manipulation.

How Sure Is Sure? Incorporating Human Error Into Machine Learning

How to Make Artificial Intelligence More Democratic

Scientific American How to Make Artificial Intelligence More Democratic A new type of learning model uses far less data than conventional AIs, allowing researchers with limited resources to contribute Advertisement This year, GPT-3, a large language model capable of understanding text, responding to questions and generating new writing examples, has drawn international media attention. The model, released by OpenAI, a California-based nonprofit that builds general-purpose artificial intelligence systems, has an impressive ability to mimic human writing, but just as notable is its massive size. To build it, researchers collected 175 billion parameters (a type of computational unit) and more than 45 terabytes of text from Common Crawl, Reddit, Wikipedia and other sources, then trained it in a process that occupied hundreds of processing units for thousands of hours.

Breves resúmenes de los 25 mejores artículos publicados en Tech Xplorer en el 2020 (Primera parte).